code: def stats(
      policy: Policy, train_batch: SampleBatch
  ) -> Dict[str, TensorType]:
      """Stats function for SAC. Returns a dict with important loss stats.
  
      Args:
          policy: The Policy to generate stats for.
          train_batch: The SampleBatch (already) used for training.
  
      Returns:
          Dict[str, TensorType]: The stats dict.
      """
      q_t = torch.stack(policy.get_tower_stats("q_t"))
  
      return {
          "actor_loss": torch.mean(
              torch.stack(policy.get_tower_stats("actor_loss"))
          ),
          "critic_loss": torch.mean(
              torch.stack(
                  tree.flatten(policy.get_tower_stats("critic_loss"))
              )
          ),
          "alpha_loss": torch.mean(
              torch.stack(policy.get_tower_stats("alpha_loss"))
          ),
          "alpha_value": torch.exp(policy.model.log_alpha),
          "log_alpha_value": policy.model.log_alpha,
          "target_entropy": policy.model.target_entropy,
          "policy_t": torch.mean(
              torch.stack(policy.get_tower_stats("policy_t"))
          ),
          "mean_q": torch.mean(q_t),
          "max_q": torch.max(q_t),
          "min_q": torch.min(q_t),
      }
  
imports: import gymnasium
  from gymnasium.spaces import Box, Discrete
  import logging
  import tree
  from typing import Dict, List, Optional, Tuple, Type, Union
  import ray
  import ray.experimental.tf_utils
  from ray.rllib.algorithms.sac.sac_tf_policy import build_sac_model, postprocess_trajectory, validate_spaces
  from ray.rllib.algorithms.dqn.dqn_tf_policy import PRIO_WEIGHTS
  from ray.rllib.models.catalog import ModelCatalog
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.models.torch.torch_action_dist import TorchCategorical, TorchDistributionWrapper, TorchDirichlet, TorchSquashedGaussian, TorchDiagGaussian, TorchBeta
  from ray.rllib.policy.policy import Policy
  from ray.rllib.policy.policy_template import build_policy_class
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.utils.framework import try_import_torch
  from ray.rllib.utils.spaces.simplex import Simplex
  from ray.rllib.policy.torch_mixins import TargetNetworkMixin
  from ray.rllib.utils.torch_utils import apply_grad_clipping, concat_multi_gpu_td_errors, huber_loss
  from ray.rllib.utils.typing import LocalOptimizer, ModelInputDict, TensorType, AlgorithmConfigDict
label_desc: Use image node_id 5 for calling a global function with example usage: stats(policy, train_batch) and returns: dict